划分区间 bins = [0,1,2,3,4,5,10,15,20,25,30,35,40,45,50,60,70,80,90,100,1220] data=data_forecast_is_stk_bs_rpt_zq_befor_big['本年比上一年归母倍数'] # 统计每个柱子的数据 hist, edges = pd.cut(data, bins=bins, right=False, include_lowest=True, retbins=True) counts = hist.value_counts() # 按照 bins 的顺序给每个柱子指定标签 labels = [f'{bins[i]}-{bins[i+1]}' for i in range(len(bins)-1)] fig, ax = plt.subplots(figsize=(12, 6)) # 绘制柱形图 plt.bar(counts.index.astype(str), counts.values) # 添加数据标签 for i, v in enumerate(counts.values): plt.text(i, v, str(v), ha='center', va='bottom') # 设置图表标题和坐标轴标签 plt.title('Score Distribution') plt.xlabel('Range') plt.ylabel('Count') # 显示图表 plt.show() 柱形图从左到右是按数量从大到小显示的顺序,能不能按bins中的顺序从左往右显示
时间: 2023-12-14 07:36:36 浏览: 24
可以按照 bins 中的顺序从左往右显示,只需要在绘制柱形图时指定 x 轴的标签为 labels 即可。修改代码如下:
```
fig, ax = plt.subplots(figsize=(12, 6))
# 绘制柱形图,并按照 labels 的顺序指定 x 轴的标签
plt.bar(labels, counts[labels])
# 添加数据标签
for i, v in enumerate(counts[labels]):
plt.text(i, v, str(v), ha='center', va='bottom')
# 设置图表标题和坐标轴标签
plt.title('Score Distribution')
plt.xlabel('Range')
plt.ylabel('Count')
# 显示图表
plt.show()
```
相关问题
# 划分区间 bins = [0,1,2,3,4,5,10,15,20,25,30,35,40,45,50,60,70,80,90,100,1220] data = data_forecast_is_stk_bs_rpt_zq_befor_big['本年比上一年归母倍数'] data1 = data_forecast_is_stk_bs_rpt_cw_befor_big['本年比上一年归母倍数'] # 统计每个柱子的数据 hist, edges = pd.cut(data, bins=bins, right=False, include_lowest=True, retbins=True) hist1, edges1 = pd.cut(data1, bins=bins, right=False, include_lowest=True, retbins=True) counts = hist.value_counts() counts1 = hist1.value_counts() # 按照 hist 的顺序重新排序 counts counts = counts.reindex(hist.cat.categories) counts1 = counts1.reindex(hist1.cat.categories) # 按照 bins 的顺序给每个柱子指定标签 labels = counts.index.astype(str) labels1 = counts1.index.astype(str) fig, ax = plt.subplots(figsize=(18, 6)) # 绘制柱形图 #plt.bar(labels, counts) plt.bar(labels, counts, width=0.4, align='center', label='Data 1') plt.bar(labels1+0.4, counts1, width=0.4, align='center', label='Data 2') can only concatenate str (not "float") to str
这个错误提示是因为在下面的代码中,你使用了字符串类型的标签和浮点类型的数值进行字符串拼接,导致了这个错误。具体来说,在第 19 行中,你使用了 `labels1+0.4` 来进行标签的位置调整,但是 `labels1` 是一个字符串类型的数组,无法直接和浮点数相加。
你可以将 `labels1` 数组中的每个元素都加上 0.4,然后再进行字符串拼接,例如:
```python
labels1 = (edges[:-1] + 0.4).astype(str) + '-' + (edges[1:]).astype(str)
```
这样就可以得到一个类似于 `'0.4-1.0'` 的字符串,表示某个区间的标签。然后在绘制柱形图时,就可以使用这个新的标签数组 `labels1`。
完整代码如下:
```python
# 划分区间
bins = [0,1,2,3,4,5,10,15,20,25,30,35,40,45,50,60,70,80,90,100,1220]
data = data_forecast_is_stk_bs_rpt_zq_befor_big['本年比上一年归母倍数']
data1 = data_forecast_is_stk_bs_rpt_cw_befor_big['本年比上一年归母倍数']
# 统计每个柱子的数据
hist, edges = pd.cut(data, bins=bins, right=False, include_lowest=True, retbins=True)
hist1, edges1 = pd.cut(data1, bins=bins, right=False, include_lowest=True, retbins=True)
counts = hist.value_counts()
counts1 = hist1.value_counts()
# 按照 hist 的顺序重新排序 counts
counts = counts.reindex(hist.cat.categories)
counts1 = counts1.reindex(hist1.cat.categories)
# 按照 bins 的顺序给每个柱子指定标签
labels = counts.index.astype(str)
labels1 = (edges[:-1] + 0.4).astype(str) + '-' + (edges[1:]).astype(str)
fig, ax = plt.subplots(figsize=(18, 6))
# 绘制柱形图
plt.bar(labels, counts, width=0.4, align='center', label='Data 1')
plt.bar(labels1, counts1, width=0.4, align='center', label='Data 2')
# 添加图例和标题
plt.legend()
plt.title('Histogram of Data')
```
# 划分区间 bins = [0,1,2,3,4,5,10,15,20,25,30,35,40,45,50,60,70,80,90,100,1220] data=data_forecast_is_stk_bs_rpt_zq_befor_big['本年比上一年归母倍数'] # 统计每个柱子的数据 hist, edges = pd.cut(data, bins=bins, right=False, include_lowest=True, retbins=True) counts = hist.value_counts() # 按照 bins 的顺序给每个柱子指定标签 labels = [f'{bins[i]}-{bins[i+1]}' for i in range(len(bins)-1)] fig, ax = plt.subplots(figsize=(18, 6)) # 绘制柱形图 plt.bar(counts.index.astype(str), counts[labels]) # 添加数据标签 for i, v in enumerate(counts.values): plt.text(i, v, str(v), ha='center', va='bottom') # 设置图表标题和坐标轴标签 plt.title('Score Distribution') plt.xlabel('Range') plt.ylabel('Count') # 显示图表 plt.show() KeyError: "None of [['0-1', '1-2', '2-3', '3-4', '4-5', '5-10', '10-15', '15-20', '20-25', '25-30', '30-35', '35-40', '40-45', '45-50', '50-60', '60-70', '70-80', '80-90', '90-100', '100-1220']] are in the [index]"
这个 KeyError 的原因是因为 hist.value_counts() 方法返回的是一个以区间标签作为索引的 Series,而在 plt.bar(counts.index.astype(str), counts[labels]) 中使用了 counts[labels],这里的 labels 是一个列表,不是一个合法的索引。可以将 labels 改为 counts.index.astype(str),即可解决该问题。修改后的代码如下:
```
# 划分区间
bins = [0,1,2,3,4,5,10,15,20,25,30,35,40,45,50,60,70,80,90,100,1220]
data = data_forecast_is_stk_bs_rpt_zq_befor_big['本年比上一年归母倍数']
# 统计每个柱子的数据
hist, edges = pd.cut(data, bins=bins, right=False, include_lowest=True, retbins=True)
counts = hist.value_counts()
# 按照 bins 的顺序给每个柱子指定标签
labels = counts.index.astype(str)
fig, ax = plt.subplots(figsize=(18, 6))
# 绘制柱形图
plt.bar(labels, counts)
# 添加数据标签
for i, v in enumerate(counts.values):
plt.text(i, v, str(v), ha='center', va='bottom')
# 设置图表标题和坐标轴标签
plt.title('Score Distribution')
plt.xlabel('Range')
plt.ylabel('Count')
# 显示图表
plt.show()
```